@inproceedings{koto-etal-2022-cloze,
title = "Cloze Evaluation for Deeper Understanding of Commonsense Stories in {I}ndonesian",
author = "Koto, Fajri and
Baldwin, Timothy and
Lau, Jey Han",
editor = "Bosselut, Antoine and
Li, Xiang and
Lin, Bill Yuchen and
Shwartz, Vered and
Majumder, Bodhisattwa Prasad and
Lal, Yash Kumar and
Rudinger, Rachel and
Ren, Xiang and
Tandon, Niket and
Zouhar, Vil{\'e}m",
booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.csrr-1.2",
doi = "10.18653/v1/2022.csrr-1.2",
pages = "8--16",
abstract = "Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending. To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and (2) a generation task to complete the story with a single sentence. We investigate these tasks in two settings: (i) monolingual training and ii) zero-shot cross-lingual transfer between Indonesian and English.",
}
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<abstract>Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending. To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and (2) a generation task to complete the story with a single sentence. We investigate these tasks in two settings: (i) monolingual training and ii) zero-shot cross-lingual transfer between Indonesian and English.</abstract>
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%0 Conference Proceedings
%T Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian
%A Koto, Fajri
%A Baldwin, Timothy
%A Lau, Jey Han
%Y Bosselut, Antoine
%Y Li, Xiang
%Y Lin, Bill Yuchen
%Y Shwartz, Vered
%Y Majumder, Bodhisattwa Prasad
%Y Lal, Yash Kumar
%Y Rudinger, Rachel
%Y Ren, Xiang
%Y Tandon, Niket
%Y Zouhar, Vilém
%S Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F koto-etal-2022-cloze
%X Story comprehension that involves complex causal and temporal relations is a critical task in NLP, but previous studies have focused predominantly on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (2016) in evaluating story understanding in Indonesian, by constructing a four-sentence story with one correct ending and one incorrect ending. To investigate commonsense knowledge acquisition in language models, we experimented with: (1) a classification task to predict the correct ending; and (2) a generation task to complete the story with a single sentence. We investigate these tasks in two settings: (i) monolingual training and ii) zero-shot cross-lingual transfer between Indonesian and English.
%R 10.18653/v1/2022.csrr-1.2
%U https://aclanthology.org/2022.csrr-1.2
%U https://doi.org/10.18653/v1/2022.csrr-1.2
%P 8-16
Markdown (Informal)
[Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian](https://aclanthology.org/2022.csrr-1.2) (Koto et al., CSRR 2022)
ACL